# PR summaries vs. PR explainability: Closing comprehension debt > Source: > Published: 2026-09-15 00:00:00+00:00 An agent-generated pull request can span dozens of files while its top summary says only: “Refactors the authentication module to improve session handling.” Passing checks and confident prose can make a fast approval feel safer than it is. If session handling later fails in production, the postmortem may trace the failure back to a change that nobody fully understood before it merged. The summary may have been accurate while still omitting the structural relationships a reviewer needed to examine. Passing checks do not prove that the pull request received a complete evaluation. The gap between a high-level summary and genuine comprehension is known as comprehension debt. This debt accumulates during review rather than code generation, and AI-assisted workflows can accelerate its growth beyond traditional review capacity. Understanding the boundary between PR summaries and PR explainability helps teams evaluate whether a review tool delivers genuine understanding or polished orientation. *Navigating comprehension debt in automated code reviews.* ## Comprehension debt and its origin Engineering leader Addy Osmani defined the term in a [March 2026 essay](https://medium.com/@addyosmani/comprehension-debt-the-hidden-cost-of-ai-generated-code-285a25dac57e). Comprehension debt is the growing gap between the amount of code in a system and how much of it people genuinely understand. The debt can expand during review because generated code often executes successfully, which can lower reviewer scrutiny. Review has two concurrent responsibilities: it is a quality gate for production deployments and a primary mechanism for distributing system knowledge across an engineering team. At current agent generation speeds, unit tests can pass and diffs can appear clean while team mental models degrade. Standard review workflows frequently struggle with both responsibilities. ## The scope of a PR summary A [PR summary](https://docs.coderabbit.ai/pr-reviews/summaries) groups changes by type and helps reviewers understand the scope of a pull request before they inspect individual files. Agents generate these descriptions rapidly. However, a summary orients a reviewer; it does not substitute for comprehension. It describes the change without proving that the implementation fits the system architecture. Summaries have structural limits. They report modifications while leaving reviewers to determine systemic correctness, cross-function dependencies, and assumptions that may have become obsolete. A well-formatted AI summary can project authority and create a sense of clarity that encourages quick approval. A typical structured summary follows: | Refactors `AuthSession` handling for clarity and testability. Extracts token validation into`validateSession()` . Consolidates expiry checks into a single code path. Updates six call sites to use the new signature. Adds unit tests for happy-path and expired-token cases. | Every line may be accurate. Yet the text leaves open whether the consolidated expiry logic retains critical refresh-token edge cases or whether updated call sites now accept null tokens that were previously restricted. ## Defining PR explainability [PR explainability](https://www.coderabbit.ai/guides/pull-request-explainability-agentic-sdlc) provides evidence that helps reviewers connect implementation details to intent, system dependencies, runtime behavior, and architectural risk. That evidence can span tickets, source code, test suites, historical pull requests, architecture decision records, and static review findings. An explainable review interface organizes these sources around the active change and maintains traceability between each explanation and the implementation. After a review, three questions test whether the interface delivered comprehension: 1. What systemic impact does this change introduce beyond isolated file modifications? 2. Does the implementation match the intent established in the linked ticket? 3. Will future engineers be able to trace the rationale behind this code path during an incident? Uncertainty on any of these points indicates a review limited to surface summaries rather than explainable analysis. CodeRabbit frames [agent explainability](https://www.coderabbit.ai/guides/ai-agent-explainability) around concrete questions: what changed, what the agent checked, what it did not check, how confident it is, and what would change its recommendation. Each answer should tie back to the diff. At the pull request layer, findings stay attributable and connected to specific line ranges. The distinction resembles monitoring and observability in infrastructure. Monitoring reports active events; observability helps engineers understand why a complex system behaves as it does. Agentic software development needs comparable visibility before code merges. ## Factors accelerating comprehension gaps Human authors can supply reasoning through commit histories, design discussions, and direct context. Agent-generated code may arrive without that background and optimize for localized correctness within the context it received. Reviewers are then left without the historical rationale needed to evaluate architectural choices. Reviewer biases compound the challenge. A 2026 Mining Software Repositories study, [“More Code, Less Reuse”](https://arxiv.org/abs/2601.21276), found that the Average Max Redundancy of agent-written pull requests was nearly 1.87 times that of human-authored pull requests. The same study found that reviewers expressed more neutral or positive sentiment toward AI-generated contributions, despite the higher redundancy. Surface plausibility can conceal structural duplication and allow silent technical debt to pass through normal review checkpoints. Mitigating these biases requires review interfaces that surface durable, inspectable evidence throughout the evaluation workflow. ## Core capabilities of explainable review interfaces An explainable interface organizes three types of review evidence for direct verification. ### Dependency-aware reading order Complex changes follow structural hierarchies: foundational components establish definitions, while higher-level features build on them. Reviewers gain context by evaluating code in this logical sequence. Standard alphabetical file lists obscure the hierarchy and force reviewers to reconstruct relationships across dozens of files. Interfaces with features such as CodeRabbit’s [Change Stack](https://docs.coderabbit.ai/change-stack) perform this reconstruction automatically. Change Stack organizes edits into functional cohorts and ordered layers tied to explicit line ranges, so foundational updates such as data schemas appear before the application logic that consumes them. This grouping presents a concrete claim about code relationships that reviewers can verify or challenge. ### Decision-linked findings Two comments can address the same defect at different levels of explanation. The first states: “Null check missing on line 42.” The second states: “Null check missing on line 42; this function executes across three paths in the auth module, two of which accept a null session token following an earlier timeout change.” Both identify the bug, but only the second explains the system exposure and deployment risk. The first highlights an isolated symptom and requires the reviewer to trace downstream effects manually. The second performs that dependency tracing directly and turns issue detection into system understanding. ### Prioritizing behavioral modifications Large diffs include non-functional updates such as renames, import adjustments, and reformatting. Core logic changes can remain buried within these mechanical edits. Semantic diff tools isolate functional changes and direct reviewer attention to executable behavior rather than whitespace churn. Block summaries can add plain-language context for functional updates, while visual diagrams clarify complex call flows or state lifecycles when textual diffs become dense. Each visualization should answer a structural question rather than repeat the diff. Apply the three-question test to an explainable interface and it produces precise, inspectable answers. A standard summary leaves system impact ambiguous. ## Impact on engineering performance Looking back at a code decision six months after merge reveals whether a team preserved architectural context or retained only a surface summary. Explainability shifts review time away from reconstructing diff structure and toward evaluating technical correctness and design strategy. Industry data shows why this matters. [CodeRabbit’s analysis](https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report) of 470 open-source pull requests found that AI-authored PRs contained about 1.7 times more issues than human-authored PRs: 10.83 findings per PR compared with 6.45. [Salesforce Engineering](https://engineering.salesforce.com/scaling-code-reviews-adapting-to-a-surge-in-ai-generated-code/) observed an approximately 30% increase in code volume, with pull requests regularly exceeding 20 files and 1,000 lines. Review time for its largest pull requests began to plateau or decline. Moving beyond polished summaries requires review interfaces designed around explainability. Summaries are an output. Explainability is a property of how the review interface organizes code relationships, evidence, and decisions. ## Strategic perspective Comprehension debt surfaces in post-incident analysis, prolonged review cycles, and rapid approvals that produce failures weeks later. A polished summary creates an impression of understanding while core logic remains unverified. Addressing the gap requires review interfaces that present changes through dependency ordering, explicit reasoning, and noise reduction. During your next pull request review, ask what the change introduces to the broader system, whether it fulfills the ticket requirements, and whether future teams can trace the underlying design rationale. Clear answers indicate genuine comprehension. Ambiguous answers leave the work of understanding unresolved.